{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# `prettyplotlib.boxplot`\n",
      "\n",
      "The original matplotlib boxplots are okay, but their lines are too thick and the blues and reds are a little garish."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "np.random.seed(10)\n",
      "\n",
      "data = np.random.randn(8, 4)\n",
      "labels = ['A', 'B', 'C', 'D']\n",
      "\n",
      "fig, ax = plt.subplots()\n",
      "ax.boxplot(data)\n",
      "ax.set_xticklabels(labels)\n",
      "fig.savefig('boxplot_matplotlib_default.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7evSpU6d0zz336NKlS6qqqlJpaakqKytjtq0fdQQjFAopFApNUD4AZBbDMGQYxoTtTE3L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\nqldzuXl/mwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ca96090>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In `prettyplotlib`, we did the usual softening of the blacks and removing of the axes. We also changed the colors of the boxplot, which is actually really annoying to do by hand:\n",
      "\n",
      "    import brewer2mpl\n",
      "    set1 = brewer2mpl.get_map('Set1', 'qualitative', 7).mpl_colors\n",
      "\n",
      "    bp = ax.boxplot(x, **kwargs)\n",
      "    plt.setp(bp['boxes'], color=set1[1], linewidth=0.5)\n",
      "    plt.setp(bp['medians'], color=set1[0])\n",
      "    plt.setp(bp['whiskers'], color=set1[1], linestyle='solid', linewidth=0.5)\n",
      "    plt.setp(bp['fliers'], color=set1[1])\n",
      "    plt.setp(bp['caps'], color='none')\n",
      "\n",
      "So using `prettyplotlib.boxplot` is much easier."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext autoreload\n",
      "%autoreload 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The autoreload extension is already loaded. To reload it, use:\n",
        "  %reload_ext autoreload\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import matplotlib as mpl\n",
      "\n",
      "np.random.seed(10)\n",
      "\n",
      "data = np.random.randn(8, 4)\n",
      "labels = ['A', 'B', 'C', 'D']\n",
      "\n",
      "fig, ax = plt.subplots()\n",
      "ppl.boxplot(ax, data, xticklabels=labels)\n",
      "fig.savefig('boxplot_prettyplotlib_default.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7mZmZftc4nU6lp6crNTVVklRZWanm5mZlZWUFWhYAMANBnbm73W6lpKSMHycnJ8vtdgez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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10cef3a50>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As of v0.1.4, this also works without an `ax` as the first argument!!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ppl.boxplot(data, xticklabels=labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10cfbdad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10d008a90>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}